Environmental factors and the inherent resolution of image sensors sometimes makes it difficult to achieve high-resolution images.
Super-resolution (SR) reconstruction techniques, however, can enhance the resolution of digital imaging sensors. For example, super-resolution reconstruction can use pixel values from two or more images, each of which has a resolution dictated by the physical resolution of the digital image sensor, and can enhance the resolution of the reconstructed images beyond the physical resolution of the digital image sensor. Accordingly, images captured, for example, by small, inexpensive, low-power digital image sensors can be enhanced to a level on par with a larger, more expensive, higher-power digital image sensor. Super-resolution also can be employed in other industries and applications.
Typically, super-resolution requires two or more images of a scene that are taken from slightly different viewpoints. Accordingly, each image of the same scene differs somewhat from the other images. For example, there may be a sub-pixel shift between location of an object in the images. Such a shift is sometimes referred to as motion. Motion can occur, for example, when the scene shifts slightly (e.g., multiple images of a moving object are acquired or multiple images of a scene are acquired by a moving camera) or when the images are captured by multiple cameras (such as by a multi-channel array camera). If differences between images are caused by something other than motion, typical super-resolution reconstruction techniques cannot be used because the motion estimation stage of the super-resolution techniques fails.
For example, differences between images (other than motion) can be present for images taken with multiple adjacent cameras (or channels) where each camera has a different spectral filter. The images from the channels can contain motion (because the images are acquired from slightly different viewpoints). The images from each channel also may differ because they contain different pixel values associated with a particular color of light incident on each respective channel sensor (assuming, e.g., that each channel is associated with a different spectral filter). In such a scenario, typical super-resolution reconstruction would not be successful because the different spectral characteristics of the images makes it difficult to correlate them.
Nevertheless, in some cases, imagers having multiple adjacent channels include at least one repeating spectral filter. For example, a four-channel imager may include one channel with a red filter, one channel with a blue filter, and two channels with green filters. Typical super-resolution reconstruction techniques can use the pixel values from the two channels having the repeating spectral filters (i.e., the green filters). On the other hand, the pixel values from the channels having the red and blue filters would not be used. Using such techniques, the resolution of the reconstructed image would be lower than if all four channels could be used to reconstruct the image.